Construction of an aspect-level sentiment analysis model for online medical reviews

被引：17
作者：
Zhao, Yuehua [1 ]
Zhang, Linyi [1 ]
Zeng, Chenxi [2 ]
Lu, Wenrui [3 ]
Chen, Yidan [1 ]
Fan, Tao [1 ]
机构：
[1] Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China
[2] Nanjing Univ, Dept Math, Nanjing 210093, Peoples R China
[3] Nanjing Univ, Business Sch, Nanjing 210023, Peoples R China
来源：
INFORMATION PROCESSING & MANAGEMENT | 2023年 / 60卷 / 06期
基金：
中国国家自然科学基金; 国家教育部科学基金资助;
关键词：
Online medical review; Fine-grained sentiment analysis; Aspect -level sentiment analysis; Ontology; Knowledge graph; CLASSIFICATION; SURGEONS; RATINGS;
D O I：
10.1016/j.ipm.2023.103513
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Online medical services have become increasingly popular, and patient feedback can significantly influence other patients' medical decision-making. This study utilizes a double-layer domain ontology for conducting aspect-level sentiment analysis of reviews from online medical platforms. A double-layer aspect recognition model (OMR-ARM), aggregating the knowledge of the domain ontology, is built to identify the aspects of online medical reviews. The proposed model outperforms baseline models by up to 23.12%. Incorporating this model into a series of state-of-theart models, the resultant OMR-ALSA model achieves a F1-score of 93.53% for aspect-level sentiment analysis of online medical reviews. Additionally, this study develops an objectaspect-sentiment knowledge graph of online medical reviews (OMR-KG) that can classify patients' sentimental polarities towards the different aspects of online medical reviews. The proposed model and constructed KG have the potential to provide reference and guidance to sentiment analysis research in the online medical review domain, thus contributing to more informed and personalized healthcare decision-making.
引用
收藏
页数：17
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